Dataset of BPNN-based Image Restoration Algorithm Optimized using Hybrid Genetic Algorithm

This dataset consists of a test result dataset with 10 sample images and a test result dataset with artificial images. Backpropagation neural networks (BPNNs) can be used to restore images; however, the error surface of the BPNN algorithm contains several extrema, making it easy to slip into a locally optimal solution. A genetic algorithm (GA) with a strong global searchability can optimize the initial weight and threshold of BPNNs. However, traditional GAs are prone to local convergence and stagnation; hence, we propose a hybrid GA. First, the hybrid GA introduces an elite opposition-based learning strategy to increase population diversity and avoid premature maturation. Second, the firefly al-gorithm updates mutated individuals twice. Thus, the searchability of the algorithm in the vicinity of the optimal solution is increased. The results show that the BPNN-based image restoration al-gorithm optimized using the improved genetic algorithm (IGABPR) is better than the BPNN-based image restoration (BPR) algorithm and the BPNN-based image restoration algorithm optimized using the genetic algorithm (GABPR) in terms of PSNR and MSE metrics.